ESAS: Towards Practical and Explainable Short Answer Scoring (Student Abstract)
Palak Goenka, Mehak Piplani, Ramit Sawhney, Puneet Mathur, Rajiv Ratn Shah · Proceedings of the AAAI Conference on Artificial Intelligence · 2020
Motivated by the mandate to design and deploy a practical, real-world educational tool for grading, we extensively explore linguistic patterns for Short Answer Scoring (SAS) as well as authorship feedback. We approach the SAS task via a multipronged approach that employs linguistic context features for capturing domain-specific knowledge while emphasizing on domain agnostic grading and detailed feedback via an ensemble of explainable statistical models. Our methodology quantitatively supersedes multiple automatic short answer scoring systems.